國立高雄大學圖資館 |

Language: English

Back

Transactional machine learning with ...

Maurice, Sebastian.

Transactional machine learning with data streams and AutoMLbuild frictionless and elastic machine learning solutions with Apache Kafka in the cloud using Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Transactional machine learning with data streams and AutoMLby Sebastian Maurice.
Reminder of title:	build frictionless and elastic machine learning solutions with Apache Kafka in the cloud using Python /
Author:	Maurice, Sebastian.
Published:	Berkeley, CA :Apress :2021.
Description:	xv, 276 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-7023-3
ISBN:	9781484270233$q(electronic bk.)

Transactional machine learning with data streams and AutoMLbuild frictionless and elastic machine learning solutions with Apache Kafka in the cloud using Python /
Maurice, Sebastian.

Transactional machine learning with data streams and AutoMLbuild frictionless and elastic machine learning solutions with Apache Kafka in the cloud using Python /[electronic resource] :by Sebastian Maurice. - Berkeley, CA :Apress :2021. - xv, 276 p. :ill., digital ;24 cm.

Chapter 1: Introduction: Big data, Auto Machine Learning and Data Streams -- Chapter 2: Transactional Machine Learning -- Chapter 3: Industry Challenges with Data Streams and AutoML -- Chapter 4: The Business Value of Transactional Machine Learning -- Chapter 5: The Technical Components and Architecture for Transactional Machine Learning -- Overview of a TML Solution -- Chapter 6: Template for Transactional Machine Learning Solutions -- CHAPTER 7: Visualize Your TML Model Insights: Optimization, Predictions and Anomalies -- Chapter 8: Evolution and Opportunities For Transactional Machine Learning in Almost Every Industry -- Chapter 9: Conclusion and Final Thoughts.

Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights) This book will strengthen your knowledge of the inner workings of TML solutions using data streams with auto machine learning integrated with Apache Kafka. Transactional Machine Learning with Data Streams and AutoML introduces the industry challenges with applying machine learning to data streams. You will learn the framework that will help you in choosing business problems that are best suited for TML. You will also see how to measure the business value of TML solutions. You will then learn the technical components of TML solutions, including the reference and technical architecture of a TML solution. This book also presents a TML solution template that will make it easy for you to quickly start building your own TML solutions. Specifically, you are given access to a TML Python library and integration technologies for download. You will also learn how TML will evolve in the future, and the growing need by organizations for deeper insights from data streams. By the end of the book, you will have a solid understanding of TML. You will know how to build TML solutions with all the necessary details, and all the resources at your fingertips. You will: Discover transactional machine learning Measure the business value of TML Choose TML use cases Design technical architecture of TML solutions with Apache Kafka Work with the technologies used to build TML solutions Build transactional machine learning solutions with hands-on code together with Apache Kafka in the cloud.

ISBN: 9781484270233$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-7023-3doiSubjects--Uniform Titles:

Apache Kafka (Electronic resource)
Subjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .M38 2021

Dewey Class. No.: 006.31

Transactional machine learning with data streams and AutoMLbuild frictionless and elastic machine learning solutions with Apache Kafka in the cloud using Python /
LDR:03604nmm a2200325 a 4500 001 598954
003 DE-He213
005 20210519110116.0
006 m d
007 cr nn 008maaau
008 211025s2021 cau s 0 eng d
020 $a 9781484270233$q(electronic bk.)
020 $a 9781484270226$q(paper)
024 7 $a 10.1007/978-1-4842-7023-3 $2 doi
035 $a 978-1-4842-7023-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .M38 2021
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M455 2021
100 1 $a Maurice, Sebastian. $3 892904
245 1 0 $a Transactional machine learning with data streams and AutoML $h [electronic resource] : $b build frictionless and elastic machine learning solutions with Apache Kafka in the cloud using Python / $c by Sebastian Maurice.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a xv, 276 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Introduction: Big data, Auto Machine Learning and Data Streams -- Chapter 2: Transactional Machine Learning -- Chapter 3: Industry Challenges with Data Streams and AutoML -- Chapter 4: The Business Value of Transactional Machine Learning -- Chapter 5: The Technical Components and Architecture for Transactional Machine Learning -- Overview of a TML Solution -- Chapter 6: Template for Transactional Machine Learning Solutions -- CHAPTER 7: Visualize Your TML Model Insights: Optimization, Predictions and Anomalies -- Chapter 8: Evolution and Opportunities For Transactional Machine Learning in Almost Every Industry -- Chapter 9: Conclusion and Final Thoughts.
520 $a Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights) This book will strengthen your knowledge of the inner workings of TML solutions using data streams with auto machine learning integrated with Apache Kafka. Transactional Machine Learning with Data Streams and AutoML introduces the industry challenges with applying machine learning to data streams. You will learn the framework that will help you in choosing business problems that are best suited for TML. You will also see how to measure the business value of TML solutions. You will then learn the technical components of TML solutions, including the reference and technical architecture of a TML solution. This book also presents a TML solution template that will make it easy for you to quickly start building your own TML solutions. Specifically, you are given access to a TML Python library and integration technologies for download. You will also learn how TML will evolve in the future, and the growing need by organizations for deeper insights from data streams. By the end of the book, you will have a solid understanding of TML. You will know how to build TML solutions with all the necessary details, and all the resources at your fingertips. You will: Discover transactional machine learning Measure the business value of TML Choose TML use cases Design technical architecture of TML solutions with Apache Kafka Work with the technologies used to build TML solutions Build transactional machine learning solutions with hands-on code together with Apache Kafka in the cloud.
630 0 0 $a Apache Kafka (Electronic resource) $3 892905
650 0 $a Machine learning. $3 188639
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Python. $3 763308
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-7023-3
950 $a Professional and Applied Computing (SpringerNature-12059)